Processing apparatus, processing method, and non-transitory storage medium

ABSTRACT

The present invention provides a processing apparatus (10) including: a classification execution unit (12) that executes class classification, based on an inference device; a computation unit (13) that computes a precision of a class determined by the class classification; a processing unit (14) that outputs a class determined by the class classification and accepts a correct/incorrect input from an operator when the precision is less than a reference value; and a registration unit (15) that registers a product determined by the class classification as a purchase target when the precision is equal to or more than the reference value, and registers a product determined by a correct/incorrect input from the operator as a purchase target when the precision is less than the reference value.

TECHNICAL FIELD

The present invention relates to a processing apparatus, a processingmethod, and a program.

BACKGROUND ART

Patent Document 1 discloses that, in face recognition processing ofsearching a program video for a person to be searched, sorting that usesa precision indicating a possibility of each performer being captured ina program video is performed.

RELATED DOCUMENT Patent Document

-   [Patent Document 1] Japanese Patent Application Publication No.    2017-33372

DISCLOSURE OF THE INVENTION Technical Problem

An inference device generated by machine learning or the like maysometimes give an erroneous inference result. A technique for detectingthat there is a possibility that an inference result may be erroneousand correcting the error is desired. Such a technique can preventinconvenience that subsequent processing is performed based on anerroneous inference result. Patent Document 1 does not disclose such aproblem and a solution therefor.

The present invention addresses a problem of providing a technique fordetecting that there is a possibility that an inference result of aninference device generated by machine learning or the like may beerroneous and correcting the error.

Solution to Problem

According to the present invention, provided is a processing apparatusincluding:

a classification execution means for executing class classification,based on an inference device;

a computation means for computing a precision of a class determined bythe class classification; and

a processing means for outputting a class determined by the classclassification and accepting a correct/incorrect input from an operatorwhen the precision is less than a reference value.

Further, according to the present invention, provided is a processingmethod including:

by a computer,

executing class classification, based on an inference device;

computing a precision of a class determined by the class classification;and

outputting a class determined by the class classification and acceptinga correct/incorrect input from an operator when the precision is lessthan a reference value.

Further, according to the present invention, provided is a programcausing a computer to function as:

a classification execution means for executing class classification,based on an inference device;

a computation means for computing a precision of a class determined bythe class classification; and

a processing means for outputting a class determined by the classclassification and accepting a correct/incorrect input from an operatorwhen the precision is less than a reference value.

Advantageous Effects of Invention

According to the present invention, a technique for detecting that thereis a possibility that an inference result of an inference devicegenerated by machine learning or the like may be erroneous andcorrecting the error is achieved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating one example of a hardware configurationof a processing apparatus according to a present example embodiment.

FIG. 2 is one example of a function block diagram of the processingapparatus according to the present example embodiment.

FIG. 3 is a diagram schematically illustrating one example ofinformation processed by the processing apparatus according to thepresent example embodiment.

FIG. 4 is a diagram schematically illustrating one example ofinformation processed by the processing apparatus according to thepresent example embodiment.

FIG. 5 is a diagram schematically illustrating one example of a screenoutput by the processing apparatus according to the present exampleembodiment.

FIG. 6 is a diagram schematically illustrating one example of a screenoutput by the processing apparatus according to the present exampleembodiment.

FIG. 7 is a diagram schematically illustrating one example of a screenoutput by the processing apparatus according to the present exampleembodiment.

FIG. 8 is a flowchart illustrating one example of a flow of processingof the processing apparatus according to the present example embodiment.

FIG. 9 is a flowchart illustrating one example of a flow of processingof the processing apparatus according to the present example embodiment.

FIG. 10 is a diagram schematically illustrating one example ofinformation processed by the processing apparatus according to thepresent example embodiment.

FIG. 11 is a diagram schematically illustrating one example ofinformation processed by the processing apparatus according to thepresent example embodiment.

FIG. 12 is a diagram schematically illustrating one example ofinformation processed by the processing apparatus according to thepresent example embodiment.

FIG. 13 is a diagram schematically illustrating one example ofinformation processed by the processing apparatus according to thepresent example embodiment.

DESCRIPTION OF EMBODIMENTS First Example Embodiment Overview

First, an overview of processing executed by a processing apparatusaccording to a present example embodiment will be described. Theprocessing apparatus executes class classification, based on aninference device generated by machine learning or the like, and thencomputes a precision of a class determined by the class classification,based on a prior distribution of the determined class. Then, when theprecision is equal to or more than a reference value, the processingapparatus executes subsequent processing, based on the determined class.On the other hand, when the precision is less than a reference value,the processing apparatus outputs the determined class and accepts acorrect/incorrect input from an operator, and executes subsequentprocessing, based on a correct class determined by the correct/incorrectinput.

An application scene of the processing apparatus according to thepresent example embodiment will be described. The processing apparatusaccording to the present example embodiment is used in accountingprocessing at a store such as a convenience store or a supermarket. Oneexample of the processing apparatus according to the present exampleembodiment is a so-called point of sales (POS) register. The processingapparatus executes, based on an image photographed of a purchase targetproduct and an inference device, class classification for determining aproduct included in the image. Then, the processing apparatus computes aprecision of the determined product, based on a prior distributionindicating frequency (purchase frequency) with which the determinedproduct is registered as a purchase target. Then, when the precision isequal to or more than a reference value, a processing apparatus 10registers the determined product as a purchase target. On the otherhand, when the precision is less than a reference value, the processingapparatus 10 outputs a determination result and accepts acorrect/incorrect input from an operator, and registers a productdetermined by the correct/incorrect input as a purchase target.

The processing apparatus according to the present example embodiment asdescribed above achieves a technique for detecting that there is apossibility that an inference result of an inference device generated bymachine learning or the like may be erroneous and correcting the error.Consequently, inconvenience that subsequent processing is performedbased on an erroneous inference result can be prevented. In a case ofthe above-described application scene, inconvenience that a productdifferent from a product included in an image is registered as apurchase target and accounting processing is performed is prevented.

“Hardware Configuration”

Next, one example of a hardware configuration of the processingapparatus will be described. Each function unit of the processingapparatus is achieved by any combination of hardware and software,mainly including a central processing unit (CPU) of any computer, amemory, a program to be loaded in a memory, a storage unit (in which aprogram downloaded from a storage medium such as a compact disc (CD), aserver on the Internet, or the like can be stored as well as a programstored in advance in a stage of shipping the apparatus) such as a harddisk for storing the program, and an interface for network connection.Then, it should be understood by a person skilled in the art that thereare a variety of modification examples for a method or an apparatus forachieving the same.

FIG. 1 is a block diagram illustrating a hardware configuration of theprocessing apparatus. As illustrated in FIG. 1 , the processingapparatus includes a processor 1A, a memory 2A, an input/outputinterface 3A, a peripheral circuit 4A, and a bus 5A. The peripheralcircuit 4A includes various modules. The processing apparatus may notinclude the peripheral circuit 4A. Note that, the processing apparatusmay be configured by a plurality of physically and/or logicallyseparated apparatuses, or may be configured by one physically and/orlogically unified apparatus. When the processing apparatus is configuredby a plurality of physically and/or logically separated apparatuses, theplurality of individual apparatuses can include the above hardwareconfiguration.

The bus 5A is a data transmission path through which the processor 1A,the memory 2A, the peripheral circuit 4A, and the input/output interface3A transmit and receive data to and from one another. The processor 1Ais an arithmetic processing apparatus such as, for example, a CPU or agraphics processing unit (GPU). The memory 2A is a memory such as, forexample, a random access memory (RAM) or a read only memory (ROM). Theinput/output interface 3A includes an interface for acquiringinformation from an input apparatus, an external apparatus, an externalserver, an external sensor, a camera, and the like, an interface foroutputting information to an output apparatus, an external apparatus, anexternal server, and the like, and the like. The input apparatus is, forexample, a keyboard, a mouse, a microphone, a physical button, a touchpanel, and the like. The output apparatus is, for example, a display, aspeaker, a printer, a mailer, and the like. The processor 1A can give aninstruction to each module to perform an operation, based on anoperation result thereof

“Function Configuration”

Next, a function configuration of the processing apparatus will bedescribed. As illustrated in FIG. 2 , a processing apparatus 10 includesa storage unit 11, a classification execution unit 12, a computationunit 13, a processing unit 14, and a registration unit 15.

The storage unit 11 stores various kinds of information necessary forprocessing in the present example embodiment. For example, the storageunit 11 stores an inference device (estimation model) that determines aproduct included in an image. The inference device determines a productincluded in an image. For example, the inference device is generated bymachine learning based on training data in which a product image isassociated with product identification information (product name,product code, or the like).

The classification execution unit 12 executes class classification,based on the inference device. First, the classification execution unit12 acquires an image photographed of a purchase target product. Thephotographing is performed by an operator (example: a store clerk or acustomer) who executes accounting processing. Then, the classificationexecution unit 12 determines, by the image and the class classificationbased on the inference device stored in the storage unit 11, a productincluded in the image. The classification execution unit 12 may inputthe acquired image as is to the inference device, or may input theacquired image to the inference device after predetermined processing(example: noise removal, object detection, background removal, trimming,or the like) is performed thereon. An inference result of the inferencedevice indicates product identification information or the like on aproduct determined as a product included in an image. Note that, aconfiguration of a camera for photographing a product, a photographingmethod, a method of inputting an image to the processing apparatus 10,or the like is a matter of design choice, and any technique can beemployed.

The computation unit 13 computes a precision of a class determined bythe class classification of the classification execution unit 12. Inother words, the computation unit 13 computes a precision of a productdetermined as a product included in an image.

Herein, a detail of processing of computing a precision will bedescribed. The computation unit 13 computes a precision of a class(product) determined by class classification, based on a priordistribution of the class. Specifically, the computation unit 13computes a precision P, based on a following equation (1).

[Mathematical1] $\begin{matrix}{P = \frac{pR}{{pR} + {\left( {1 - p} \right)\left( {1 - S} \right)}}} & {{Equation}(1)}\end{matrix}$

R is a recall, and S is specificity. In the present example embodiment,the recall R and the specificity S are fixed values. In other words, therecall R and the specificity S are identical values, regardless of anenvironment (time of day, weather, or the like) at a time of accountingprocessing or a result of class classification. p is a priordistribution. In the present example embodiment, the prior distributionp indicates frequency with which a determined product is registered bythe processing apparatus 10 as a purchase target. The prior distributionp is computed based on a registration history of each product at a timeof accounting processing for the product.

In other words, the computation unit 13 retrieves a value of the recallR and the specificity S stored in advance in the storage unit 11.Further, the computation unit 13 computes the latest prior distributionp of a determined product, based on a registration history of eachproduct at a time of accounting processing for the product. Then, thecomputation unit 13 inputs, to the above equation (1), the retrievedrecall R and the specificity S and the computed prior distribution p tocompute the precision P.

Herein, the above equation (1) will be described. First, followingparameters are defined.

TP: true positive: the number of correct answers inferred to be correctFP: false positive: the number of incorrect answers inferred to becorrectFN: false negative: the number of correct answers inferred to beincorrectTN: true negative: the number of incorrect answers inferred to beincorrect

In general, it is known that the precision P, the recall R, thespecificity S, and the prior distribution p are computed by followingequations (2) to (5). The above equation (1) is acquired by modifyingthe equations.

-   [Mathematical 2]

P=TP/(TP+FP)  Equation (2)

[Mathematical 3]

R=TP/(TP+FN)  Equation(3)

[Mathematical 4]

S=TN/(TN+FP)  Equation (4)

[Mathematical 5]

p=(TP+FN)/(TP+FN+TN+FP)  Equation (5)

Next, setting the recall R and the specificity S as fixed values and amethod of determining the value will be described.

The recall R and the specificity S vary depending on difficulty ofrecognition by an inference device. The difficulty of recognitionlargely depends on how an inference target included in an image iscaptured, and differs depending on, for example, an optical environment(light from outside, indoor light, type of indoor light, a relativerelationship (distance, direction, or the like) between light and asubject, or the like) at a time of photographing, a relativerelationship (distance, direction, or the like) between an inferencetarget (subject) and a camera, and the like.

However, in a case of the present example embodiment in which theprocessing apparatus 10 is used in accounting processing at a store, itcan be assumed that the difficulty of recognition is invariableregardless of an execution environment. In other words, in a case of thepresent example embodiment, light giving influence on photographing isindoor light, and influence of light from outside is ignorable. Then,intensity or the like of indoor light is generally invariable regardlessof time or the like. Further, a room light or a camera for use inaccounting processing is generally fixed at a predetermined position,and thus, a relative relationship (distance, direction, or the like)therebetween is generally invariable regardless of time or the like.Furthermore, a method of photographing a purchase target product with acamera at a time of accounting processing is manualized, and thus, arelative relationship (distance, direction, or the like) between aninference target (subject) and a camera does not largely vary even withsome variation by each operator.

The present inventor newly found that the present example embodiment isin such an environment where the recall R and the specificity S can beassumed as invariable, and newly conceived of setting the values asfixed values.

A value of the recall R and the specificity S can be computed, forexample, based on a result of a test for the class classification of theclassification execution unit 12, executed at a predetermined placebefore the processing apparatus 10 is actually used at a store, and theabove equations (3) and (4). A place for executing the test ispreferably a store, but may be another place.

Next, a method of computing the prior distribution p will be described.In a case of the present example embodiment in which the processingapparatus 10 is used in accounting processing at a store, the priordistribution p indicates frequency (purchase frequency) with which aproduct determined by class classification is registered as a purchasetarget.

For example, the prior distribution p in the present example embodimentis a ratio of “the number of times registering a product determined byclass classification as a purchase target” to “the total number of timesregistering individual products being handled at a store as a purchasetarget”. “Registration as a purchase target” herein is processingachieved by the registration unit 15 to be described below, and isregistration as a purchase target with an accounting apparatus such as aPOS register at a time of accounting processing.

The storage unit 11 stores prior distribution information in whichproduct identification information on individual products being handledat a store is associated with the number of times each product isregistered as a purchase target, as illustrated in FIG. 3 . The priordistribution information is updated every time accounting processing isperformed. Then, the computation unit 13 computes the above-describedprior distribution p, based on the prior distribution information.

Note that, “the total number of times registering individual productsbeing handled at a store as a purchase target” may be the total numberof times registering all of products being handled at the store.

Besides the above, “the total number of times registering individualproducts being handled at a store as a purchase target” may be the totalnumber of times registering a part of products being handled at thestore. A part of products can be, for example, a group of products (aproduct group having a similar appearance) that have a similarappearance to a product determined by class classification and arerelatively difficult to distinguish from each other by the classclassification of the classification execution unit 12. A group ofproducts that are relatively difficult to distinguish from each other byclass classification may be, for example, a group of products(beverages, shampoos, toothpastes, or the like) of a same kind, may be agroup of products of a same kind of a same manufacturer, may be a groupof products of a same series of a same manufacturer, or may be others.

The storage unit 11 may store group information indicating a group ofproducts that are relatively difficult to distinguish from each other bythe class classification of the classification execution unit 12, asillustrated in FIG. 4 . Then, the computation unit 13 may determine,based on the group information, a product associated with a same groupnumber as a product determined by class classification, and may computethe total “number of times registering a product (including a productdetermined by class classification) associated with a same group numberas a product determined by class classification, as “the total number oftimes registering individual products being handled at a store as apurchase target”.

Note that, in the present example embodiment, the frequency is frequencywith which each product is registered as a purchase target at one storein which the processing apparatus 10 is installed. In other words, thefrequency indicates purchase frequency of each product at the store inwhich the processing apparatus 10 is installed. As a modificationexample, the frequency may be frequency with which the product isregistered as a purchase target at a group of a plurality of stores. Inother words, the frequency may be purchase product frequency of eachproduct at all of a plurality of stores. A group of a plurality ofstores is preferably a group of stores having a similar tendency of aproduct purchased by a customer, and examples thereof include, forexample, a group of stores having a similar location condition (example:there is a station within 100 m, there is a university within 100 m, orthe like).

Returning to FIG. 2 , the processing unit 14 outputs a class (product)determined by the class classification of the classification executionunit 12 and accepts a correct/incorrect input from an operator when theprecision P computed by the computation unit 13 is less than a referencevalue. The processing unit 14 outputs the information toward an operatorwho executes accounting processing, and accepts a correct/incorrectinput from the operator. Output of information is achieved via anyoutput apparatus such as a display, a speaker, or a projectionapparatus. Further, acceptance of an input is achieved via any inputapparatus such as a touch panel, a physical button, a microphone, amouse, or a keyboard.

For example, the processing unit 14 may display a screen as illustratedin FIG. 5 on a touch panel display, and may accept an input. On thescreen illustrated in FIG. 5 , information identifying a productdetermined by the class classification of the classification executionunit 12 is displayed, and an input answering whether the information iscorrect is accepted. Then, when accepting an input “No” on the screen,the processing unit 14 may display a screen as illustrated in FIG. 6 ona touch panel display, and may accept an input. On the screenillustrated in FIG. 6 , a list of products being handled at a store isdisplayed, and an input specifying one of the products is accepted.

Besides the above, the processing unit 14 may display a screen asillustrated in FIG. 7 on a touch panel display, and may accept an input.On the screen illustrated in FIG. 7 , a part of a plurality of productsselected from among products being handled at a store is displayed ascandidates for a purchase target product. Then, on the screen, an inputspecifying one of the candidates is accepted. When accepting an input“None of them” on the screen, the processing unit 14 may display thescreen as illustrated in FIG. 6 on a touch panel display, and may acceptan input.

Herein, one example of a method of selecting a candidate for a productto be displayed on the screen illustrated in FIG. 7 will be described.

(Selection method 1) A predetermined number of products are selected indescending order of a value of a prior distribution at that point intime.(Selection method 2) A product that is included in a predeterminednumber of products in descending order of a value of a priordistribution at that point in time and that has a value of the priordistribution equal to or more than a reference value is selected.(Selection method 3) A product determined by the class classification ofthe classification execution unit 12 is selected in addition to aproduct selected by selection method 1 or 2.(Selection method 4) A predetermined number of products are selected indescending order of a value of a prior distribution at that point intime from among products associated with a same group number as aproduct determined by the class classification of the classificationexecution unit 12.(Selection method 5) A product that is included in a predeterminednumber of products in descending order of a value of a priordistribution at that point in time among products associated with a samegroup number as a product determined by the class classification of theclassification execution unit 12 and that has a value of the priordistribution equal to or more than a reference value is selected.(Selection method 6) A product determined by the class classification ofthe classification execution unit 12 is selected in addition to aproduct selected by selection method 4 or 5.(Selection method 7) When an inference device outputs a posteriordistribution, a product is selected after replacing a value of a priordistribution for a value of the posterior distribution in selectionmethods 1 to 6.

Returning to FIG. 2 , the registration unit 15 registers a productdetermined by the class classification of the classification executionunit 12 as a purchase target when the precision P computed by thecomputation unit 13 is equal to or more than a reference value. Then,the registration unit 15 registers a product determined by acorrect/incorrect input from an operator accepted by the processing unit14 as a purchase target when the precision P is less than the referencevalue.

For example, the registration unit 15 acquires, from a product masterstored in a store server, product information (product name, unit price,or the like) on a product to be registered as a purchase target, andstores the product information in the storage unit 11.

Next, one example of a flow of processing of the processing apparatus 10will be described by using a flowchart in FIG. 8 .

In a case of the present example embodiment in which the processingapparatus 10 is used in accounting processing at a store, the processingapparatus 10 repeats product registration processing S20 of registeringa purchase target product and adjustment processing S21 based on aregistration content.

In the product registration processing S20, the processing apparatus 10repeats processing illustrated by a flowchart in FIG. 9 .

First, an operator performs an operation of photographing a purchasetarget product. The classification execution unit 12 acquires an imagegenerated by the photographing (S10).

Next, the classification execution unit 12 executes, based on aninference device stored in the storage unit 11 and the image acquired inS10, class classification for determining a product included in theimage (S11).

Next, the computation unit 13 computes the precision P of the productdetermined by the class classification in S11 (S12). Specifically, thecomputation unit 13 computes a prior distribution of the productdetermined by the class classification in S11, based on priordistribution information (see FIG. 3 ) at that point in time. Then, thecomputation unit 13 computes the precision P, based on the recall R andthe specificity S stored in advance in the storage unit 11, the computedprior distribution, and the above-described equation (1).

When the computed precision P is equal to or more than a reference value(Yes in S13), the registration unit 15 registers the product determinedby the class classification in S11 as a purchase target (S16). Forexample, the registration unit 15 acquires product information (productname, unit price, or the like) on the product from a product masterstored in a store server, and stores the product information in thestorage unit 11. Next, the registration unit 15 updates the priordistribution information (see FIG. 3 ) (S17). In other words, theregistration unit 15 increments by “1” the number of times registeringthe product registered as a purchase target.

On the other hand, when the computed precision P is less than areference value (No in S13), the processing unit 14 executescorrect/incorrect input acceptance processing of outputting the productdetermined by the class classification in S11 and accepting acorrect/incorrect input from an operator (S14). Then, the registrationunit 15 determines a correct product, based on a content of thecorrect/incorrect input in S14 (S15), and registers the productdetermined based on the content of the correct/incorrect input in S14 asa purchase target (S16). For example, the registration unit 15 acquiresproduct information (product name, unit price, or the like) on theproduct from a product master stored in a store server, and stores theproduct information in the storage unit 11. Next, the registration unit15 updates the prior distribution information (see FIG. 3 ) (S17). Inother words, the registration unit 15 increments by “1” the number oftimes registering the product registered as a purchase target.Processing in S16 and S17 is as described above.

After the processing illustrated by the flowchart in FIG. 9 is performedat least once and at least one product is registered as a purchasetarget, the processing apparatus 10 becomes able to accept an input forproceeding to adjustment processing. Then, the processing apparatus 10executes the adjustment processing S21 in response to the input.

For example, the processing apparatus 10 performs, in the adjustmentprocessing S21, adjustment for an accounting amount computed based on aregistration content in the product registration processing S20 byaccepting an input of credit card information, communicating with aserver of a credit card company to perform payment processing, acceptingan input of a deposit amount, computing change, giving computed change,executing other processing such as code payment, and issuing a receipt.

Besides the above, the processing apparatus 10 may transmit, in theadjustment processing S21, an accounting amount computed based on aregistration content in the product registration processing S20 or adetailed statement or the like of a purchase target product to anadjustment apparatus physically and/or logically separated from theprocessing apparatus 10. Then, the adjustment apparatus may accept aninput of credit card information, communicate with a server of a creditcard company to perform payment processing, accept an input of a depositamount, compute change, give computed change, execute other processingsuch as code payment, and issue a receipt. This example assumes asituation in which an apparatus for registering a purchase targetproduct and an apparatus for performing adjustment processing arephysically and/or logically separated.

“Advantageous Effect”

The processing apparatus 10 according to the present example embodimentdescribed above achieves a technique for detecting that there is apossibility that an inference result of an inference device generated bymachine learning or the like may be erroneous and correcting the error.Thus, inconvenience that subsequent processing is executed based on anerroneous inference result can be reduced. In a case of the presentexample embodiment in which the processing apparatus 10 is used inaccounting processing at a store, inconvenience that a product differentfrom a product included in an image is registered as a purchase targetand accounting processing is performed is prevented.

Further, the processing apparatus 10 according to the present exampleembodiment can compute the precision P of each product, based on a priordistribution indicating frequency with which each product is registeredas a purchase target. By using such a characteristic prior distribution,the precision P can be computed with high accuracy and an error of aninference result can be detected with high accuracy.

Further, the processing apparatus 10 according to the present exampleembodiment can compute the precision P, based on the characteristicequation (1) as described above. Thus, the highly accurate precision Pcan be computed relatively simply.

Further, the processing apparatus 10 according to the present exampleembodiment is used under an environment where the recall R and thespecificity S can be assumed as invariable regardless of an executionenvironment, and thus, the recall R and the specificity S can be set asfixed values among the parameters included in the above equation (1).Thus, burden on processing of the processing apparatus 10 can bereduced.

Second Example Embodiment

A processing apparatus 10 according to a present example embodiment isdifferent from the first example embodiment in terms of a way ofcomputing a prior distribution (purchase frequency). Specifically, theprocessing apparatus 10 determines an environment at a time ofaccounting processing, and computes a prior distribution (purchasefrequency) under the environment.

A storage unit 11 according to the present example embodiment storesprior distribution information as illustrated in FIG. 10 and environmentinformation as illustrated in FIG. 11 . As illustrated in FIG. 10 , theprior distribution information according to the present exampleembodiment indicates the number of times each product is registered as apurchase target under each environment. In other words, the priordistribution information records an environment at a time when eachproduct is registered as a purchase target. Each environment may bedetermined by one or more of parameters such as day of week, time ofday, season, weather, product manufacturer, product shape, product type,purchaser information, and stock information (stock availability orstock quantity).

A computation unit 13 first determines an environment at that time whencomputing a prior distribution and a precision P. The computation unit13 may acquire a value of various kinds of parameters determining anenvironment as described above, based on information stored in theprocessing apparatus 10, or may acquire from an external apparatus.Then, the computation unit 13 determines an environment (environmentnumber) at that time, based on the acquired value of various kinds ofparameters and the environment information as illustrated in FIG. 11 .

After determining the environment, the computation unit 13 refers to theprior distribution information as illustrated in FIG. 10 , and computesa ratio of “the number of times registering a product determined byclass classification as a purchase target under the determinedenvironment” to “the total number of times registering individualproducts being handled at a store as a purchase target under thedetermined environment”, as a prior distribution of the productdetermined by the class classification under the environment.

Other configurations of the processing apparatus 10 are similar to thefirst example embodiment.

The processing apparatus 10 according to the present example embodimentachieves an advantageous effect similar to the first example embodiment.Further, the processing apparatus according to the present exampleembodiment in which a prior distribution and the precision P of eachproduct can be computed in consideration of an environment at a time ofaccounting processing is able to more accurately detect that there is apossibility that an inference result of an inference device generated bymachine learning or the like may be erroneous.

Third Example Embodiment

A processing apparatus 10 according to a present example embodimentprepares a recall R and specificity S for each environment at a time ofaccounting processing, rather than setting as fixed values. Then, theprocessing apparatus 10 computes a precision P by using the recall R andthe specificity S associated with an environment at a time of accountingprocessing.

A storage unit 11 according to the present example embodiment stores RSinformation as illustrated in FIG. 12 and RS environment information asillustrated in FIG. 13 . As illustrated in the figures, in the presentexample embodiment, an environment at a time of accounting processing isclassified into a plurality of environments, and the recall R and thespecificity S are stored in advance in the storage unit 11 for eachenvironment. For example, a test for class classification of aclassification execution unit 12 to be performed at a predeterminedplace before the processing apparatus 10 is actually used at a store isperformed under a plurality of individual environments, and a pluralityof test results under each environment are collected. Then, the recall Rand the specificity S can be computed for each environment, based on theresult of the test under each environment and the above equations (3)and (4).

A computation unit 13 first determines an environment at that time whencomputing the precision P. The computation unit 13 may acquire a valueof various kinds of parameters determining an environment as describedabove, based on information stored in the processing apparatus 10, ormay acquire from an external apparatus. Then, the computation unit 13determines an environment (RS environment number) at that time, based onthe acquired value of various kinds of parameters and the RS environmentinformation as illustrated in FIG. 13 . After determining theenvironment, the computation unit 13 acquires the recall R and thespecificity S for use in computation of the precision P, based on the RSinformation as illustrated in FIG. 12 .

Other configurations of the processing apparatus 10 are similar to thefirst and second example embodiments.

The processing apparatus 10 according to the present example embodimentachieves an advantageous effect similar to the first and second exampleembodiments. Further, the processing apparatus 10 according to thepresent example embodiment in which the recall R and the specificity Scan be determined in consideration of an environment at a time ofaccounting processing is able to more accurately detect that there is apossibility that an inference result of an inference device generated bymachine learning or the like may be erroneous.

The processing apparatus 10 according to the present example embodimentcannot assume that the recall R and the specificity S are invariableregardless of an execution environment, but can be used under such asituation in which an execution environment is classified and the recallR and the specificity S can be assumed as invariable under eachexecution environment. In a case of the present example embodiment inwhich the processing apparatus 10 is used in accounting processing at astore, for example, a case in which the processing apparatus 10 isinstalled near a window or a door and influence of light from outside isnot ignorable for photographing of a product or the like pertains to thesituation.

Modification Example

In the first to third example embodiments, the processing apparatus 10is used in accounting processing at a store. However, a usage scene ofthe processing apparatus 10 is not limited thereto. The processingapparatus 10 according to the first to second example embodiments can beused in various kinds of scenes in which the recall R and thespecificity S can be assumed as invariable regardless of an executionenvironment. Further, the processing apparatus 10 according to the thirdexample embodiment can be used in various kinds of scenes in which anexecution environment is classified and the recall R and the specificityS can be assumed as invariable under each execution environment. Notethat, in a modification example, a processing apparatus 10 may include,in place of the registration unit 15, an execution unit that executespredetermined processing, based on a determination result of classclassification. The execution unit executes predetermined processing,based on a determination result of class classification when a precisionis equal to or more than a reference value, and executes predeterminedprocessing, based on a determination result based on a correct/incorrectinput from an operator when the precision is less than the referencevalue. A detail of predetermined processing is a matter of designchoice.

Note that, “acquisition” in the present description may include“fetching (active acquisition), by an own apparatus, data stored inanother apparatus or storage medium” based on a user input or based onan instruction of a program, for example, requesting or inquiringanother apparatus to receive data, accessing another apparatus orstorage medium to read out data therefrom, and the like. Further,“acquisition” may include “inputting (passive acquisition), to an ownapparatus, data output from another apparatus” based on a user input orbased on an instruction of a program, for example, receiving delivered(transmitted, push-notified, or the like) data, and the like. Further,“acquisition” may include selectively acquiring received data orinformation, and “generating new data by editing data (such asconversion to a text, sorting of data, extraction of partial data, orchange of a file format) or the like and acquiring the new data”.

While the present invention has been described with reference to theexample embodiments (and the examples), the present invention is notlimited to the above example embodiments (and the examples). Variousmodifications that can be understood by those skilled in the art can bemade to the configurations and details of the present invention withinthe scope of the present invention.

The whole or part of the above-described example embodiments can bedescribed as, but not limited to, the following supplementary notes.

1. A processing apparatus including:

a classification execution means for executing class classification,based on an inference device;

a computation means for computing a precision of a class determined bythe class classification; and

a processing means for outputting a class determined by the classclassification and accepting a correct/incorrect input from an operatorwhen the precision is less than a reference value.

2. The processing apparatus according to supplementary note 1, wherein

the computation means computes the precision, based on a priordistribution of a class determined by the class classification.

3. The processing apparatus according to supplementary note 2, wherein

-   -   the computation means computes the precision, based on the above        equation (1).        4. The processing apparatus according to supplementary note 3,        wherein

R and S are fixed values.

5. The processing apparatus according to supplementary note 3, wherein

a value for R and S is prepared for each environment in advance, and Rand S are determined based on an environment at a time of computing theprecision.

6. The processing apparatus according to any of supplementary notes 2 to5, wherein

the classification execution means determines, by an image photographedof a product and class classification based on the inference device, aproduct included in the image,

the processing apparatus further including

a registration means for registering a product determined by the classclassification as a purchase target when the precision is equal to ormore than the reference value, and registering a product determined by acorrect/incorrect input from the operator as a purchase target when theprecision is less than the reference value, wherein

the prior distribution indicates frequency with which a productdetermined by the class classification is registered as a purchasetarget.

7. The processing apparatus according to supplementary note 6, wherein

the prior distribution is a ratio of a number of times registering aproduct determined by the class classification as a purchase target to atotal number of times registering individual products being handled at astore as a purchase target.

8. The processing apparatus according to supplementary note 6 or 7,wherein

the computation means computes the prior distribution of a productdetermined by the class classification under an environment at a time ofcomputing the precision, based on information recording an environmentat a time when each product is registered as a purchase target, andcomputes the precision, based on the computed prior distribution.

9. A processing method including:

by a computer,

executing class classification, based on an inference device;

computing a precision of a class determined by the class classification;and

outputting a class determined by the class classification and acceptinga correct/incorrect input from an operator when the precision is lessthan a reference value.

10. A program causing a computer to function as:

a classification execution means for executing class classification,based on an inference device;

a computation means for computing a precision of a class determined bythe class classification; and

a processing means for outputting a class determined by the classclassification and accepting a correct/incorrect input from an operatorwhen the precision is less than a reference value.

What is claimed is:
 1. A processing apparatus comprising: at least onememory configured to store one or more instructions; and at least oneprocessor configured to execute the one or more instructions to: executeclass classification, based on an inference device; compute a precisionof a class determined by the class classification; and output a classdetermined by the class classification and accept a correct/incorrectinput from an operator when the precision is less than a referencevalue.
 2. The processing apparatus according to claim 1, wherein theprocessor is further configured to execute the one or more instructionsto compute the precision, based on a prior distribution of a classdetermined by the class classification.
 3. The processing apparatusaccording to claim 2, wherein the processor is further configured toexecute the one or more instructions to compute the precision, based ona following equation (1). [Mathematical1] $\begin{matrix}{P = \frac{pR}{{pR} + {\left( {1 - p} \right)\left( {1 - S} \right)}}} & {{Equation}(1)}\end{matrix}$ (Note that, P is the precision, p is the priordistribution, R is a recall, and S is specificity.)
 4. The processingapparatus according to claim 3, wherein R and S are fixed values.
 5. Theprocessing apparatus according to claim 3, wherein a value for R and Sis prepared for each environment in advance, and R and S are determinedbased on an environment at a time of computing the precision.
 6. Theprocessing apparatus according to claim 2, wherein the processor isfurther configured to execute the one or more instructions to:determine, by an image photographed of a product and classclassification based on the inference device, a product included in theimage, and register a product determined by the class classification asa purchase target when the precision is equal to or more than thereference value, and register a product determined by acorrect/incorrect input from the operator as a purchase target when theprecision is less than the reference value, wherein the priordistribution indicates frequency with which a product determined by theclass classification is registered as a purchase target.
 7. Theprocessing apparatus according to claim 6, wherein the priordistribution is a ratio of a number of times registering a productdetermined by the class classification as a purchase target to a totalnumber of times registering individual products being handled at a storeas a purchase target.
 8. The processing apparatus according to claim 6,wherein the processor is further configured to execute the one or moreinstructions to compute the prior distribution of a product determinedby the class classification under an environment at a time of computingthe precision, based on information recording an environment at a timewhen each product is registered as a purchase target, and compute theprecision, based on the computed prior distribution.
 9. A processingmethod comprising: by a computer, executing class classification, basedon an inference device; computing a precision of a class determined bythe class classification; and outputting a class determined by the classclassification and accepting a correct/incorrect input from an operatorwhen the precision is less than a reference value.
 10. A non-transitorystorage medium storing a program causing a computer to: execute classclassification, based on an inference device; compute a precision of aclass determined by the class classification; and output a classdetermined by the class classification and accept a correct/incorrectinput from an operator when the precision is less than a referencevalue.